The effective dimensionality of data segmentation problems
is given by the number of unknown parameters such that even for univariate observations,
the effective dimensionality of a given problem can be large,
see e.g. [1]} for an example for testing for parameter stability
in non-linear autoregressive models by means of a semiparametric approach
based on neural networks, which involves a large number of parameters.
To make the problem even more difficult,
statistics for revealing changes (e.g. obtained after a suitable data transformation)
are often not independent even if the observed data is.
[2]} describe in detail how testing for changes
in moderately large linear vector autoregressive processes becomes a high-dimensional problem,
what complications this results in and how to resolve them.
This shows that techniques for high-dimensional change point analysis
as discussed later in Section REF ,
are also of relevance for data segmentation under complex time series models.
